Databricks Data Intelligence Platform Reviews & Product Details

Databricks excels at unifying data engineering, analytics, and AI/ML on a single platform. The Lakehouse architecture bridges the gap between data lakes and warehouses, making it incredibly efficient for managing structured and unstructured data. I especially appreciate the seamless integration with Apache Spark, robust notebook support for collaborative development, and the simplicity of Delta Lake for versioned data storage. Features like AutoML and Unity Catalog bring governance and intelligence together, making it easier to scale analytics securely and reliably. Review collected by and hosted on G2.com.

While powerful, the platform has a learning curve—especially for teams unfamiliar with Spark or distributed computing. Some features (like Unity Catalog or serverless compute) can be region-specific or limited by cloud provider compatibility. Additionally, job debugging and cluster cost management can be challenging without careful monitoring and tagging, particularly in enterprise-scale projects. Review collected by and hosted on G2.com.

What I like best about Databricks is its seamless integration of big data processing and AI. The notebook-based interface makes collaboration easy, and the use of Spark ensures fast performance. Delta Lake also provides reliable data versioning and management, which is extremely helpful in enterprise environments. Review collected by and hosted on G2.com.

One downside is that the initial setup and networking configuration can be complex and require technical expertise. Also, the cost can scale up quickly depending on usage, so cost monitoring is essential. Additionally, the lack of comprehensive documentation in some languages like Japanese can be a limitation. Review collected by and hosted on G2.com.

What I like the best about Databricks data intelligence platforms is the uninterrupted integration of data engineering, data science and AI workflows on an single platform, which improves cooperation, robustness and performance for large data and ML projects. Review collected by and hosted on G2.com.

The lowest useful aspect of Databricks data intelligence platform is its complication for new users and steep learning state, especially for those without a strong background in spark or distributed computing. Additionally, it can be expensive on a scale, and debugging large workflows can sometimes be challenging due to limited transparency in error tracing. Review collected by and hosted on G2.com.

I like it because it stands out for its ability to unify data science, data engineering, and business analytics into a single interface. I also appreciate the seamless integration with collaborative notebooks and the ability to seamlessly with Delta Lake is also a big plus, ensuring reliability and performance when managing large-scale data. Review collected by and hosted on G2.com.

Sometimes, the web interface can take a while to load active clusters. Id also like more visual tools to monitor resource usage in real time. Review collected by and hosted on G2.com.

The Databricks Data Intelligence Platform allows us to have a single source of development capabilities for IT developers and business analysts. This allows for easier implementation of capabilities and consolidation of toolsets across the environment. Users are given the freedom to develop the data products they need in the time frames they need them. It makes implementation and productionalization of these projects much easier to integrate for downstream usage. Review collected by and hosted on G2.com.

The one "dislike" I have is that it's difficult to keep up with all of the improvements and enhancements in the platform. We always see new features to implement and want to make sure we're doing the best we can for our end users. Review collected by and hosted on G2.com.

The Databricks Data Intelligence Platform is a unified, AI-native environment that brings together data engineering, analytics, governance, and machine learning on top of the Lakehouse architecture. Its strength lies in combining open data formats with centralized governance via Unity Catalog and embedding intelligence through DatabricksIQ, which allows enterprises to securely connect their data with large language models. From an evaluation standpoint, the platform’s value is in enabling organizations to not only manage and analyze data at scale but also to operationalize generative AI use cases in a governed and collaborative manner. Review collected by and hosted on G2.com.

Current stage of evolving technology may require rebuilding on future if adapted by enterprise Review collected by and hosted on G2.com.

Databricks is very dependable, flexible, and helps our company to create innovative analytical solutions and every week in our technical meetings, we cover a range of subjects like bugs, best practices, new features, and more using it. Also every member of the support crew in Databricks responds fast and is rather helpful. Review collected by and hosted on G2.com.

When using creative features like AI, cost control and estimation might prove difficult. And although lakeview SQL is still not yet developed, Databricks is actively pushing their utilization nonetheless. Also every now and then unannounced feature activation in my office surprises me. Review collected by and hosted on G2.com.

I like its usable interface and the interactive notebooks that support SQL and Python for visualization in one place. Review collected by and hosted on G2.com.

For learners it may be difficult for beginners and cost may rise quickly Review collected by and hosted on G2.com.

Working on the Databricks where we can easily analyze huge datasets and integrate our platform or website to create an insight from our internal dataset. They have number of features that help us to manage all analytical views with pre define templates. Review collected by and hosted on G2.com.

Implementation was so quick and easy that help us to manage all data without any issues. I like their customer support services and their frequent use make them my favorite platform for data management. Review collected by and hosted on G2.com.

The best I like about snaplogic is while doing data transformation of datasets in notebooks it has many languages to use like sql,pyspark and R. This enable me to use any language and effectively complete my requirements.data bricks has many features like Ai,unity catalog workflow managements which makes my ETL implementation ,and data integration easy and quick Review collected by and hosted on G2.com.

Under heavy usage the UI responsive for some implementations like job tracking ,Notebook versioning is a bit slugguish on web Review collected by and hosted on G2.com.